Keeping the Lights On: Proactive Context Compression for PydanticAI Agents

Xavier Ferrer , Arun Narayanan and James E. Ravenscroft When you build an AI agent that calls multiple external tools in a single session and iterates over various sources of information, you eventually run into a fundamental constraint: the model can only hold so much in memory at once. Feed it enough records, API responses, and structured documents, and eventually the context window fills up, you hit the context limit and the whole execution grinds to a halt.